The goal of this proposal is to develop statistical analysis methods for environmental health data when the health effects of interest are complex. The Specific Aims are motivated by problems arising in toxicological studies and environmental epidemiological studies that investigate environmental effects that potentially result in multiple, interrelated changes over time. Empirical data analysis will play a central role in each of the Specific Aims. Environmental epidemiologists now routinely implement hierarchical study designs for an array o of biologic endpoints. A general class of hierarchical models for multilevel binary and ordinal symptom data will be developed, and the performance of these models will be compared to existing approaches known to be sensitive to modeling assumptions. Epidemiological studies have repeatedly shown associations between air particulate and increased morbidity and mortality in human populations, particularly in subjects with pre-existing respiratory or cardiac vulnerability. Current laboratory research focuses on the physiological mechanisms. Flexible models will be developed for assessing effects of exposure over time on multiple physiological endpoints.